Importance of Ligand Effects Breaking the Scaling Relation for Core-Shell Oxygen Reduction Catalysts

Precise synthetic control of exclusive ligand effect boosts oxygen reduction catalysis

Ligand effect, induced by charge transfer between catalytic surface and substrate in core/shell structure, was widely proved to benefit Pt-catalyzed oxygen reduction reaction by tuning the position of d-band center of Pt theoretically. However, ligand effect is always convoluted by strain effect in real core/shell nanostructure; therefore, it remains experimentally unknown whether and how much the ligand effect solely contributes electrocatalytic activity improvements. Herein, we report precise synthesis of a kind of Pd3Ru1/Pt core/shell nanoplates with exclusive ligand effect for oxygen reduction reaction. Layer-by-layer growth of Pt overlayers onto Pd3Ru1 nanoplates can guarantee no lattice mismatch between core and shell because the well-designed Pd3Ru1 has the same lattice parameters as Pt. Electron transfer, due to the exclusive ligand effect, from Pd3Ru1 to Pt leads to a downshift of d-band center of Pt. The optimal Pd3Ru1/Pt1-2L nanoplates achieve excellent activity and stability for oxygen reduction reaction in alkaline/acid electrolyte.

Intelligent route to design efficient CO2 reduction electrocatalysts using ANFIS optimized by GA and PSO

Recently, electrochemical reduction of CO₂ into value-added fuels has been noticed as a promising process to decrease CO₂ emissions. The development of such technology is strongly depended upon tuning the surface properties of the applied electrocatalysts. Considering the high cost and time-consuming experimental investigations, computational methods, particularly machine learning algorithms, can be the appropriate approach for efficiently screening the metal alloys as the electrocatalysts. In doing so, to represent the surface properties of the electrocatalysts numerically, d-band theory-based electronic features and intrinsic properties obtained from density functional theory (DFT) calculations were used as descriptors. Accordingly, a dataset containg 258 data points was extracted from the DFT method to use in machine learning method. The primary purpose of this study is to establish a new model through machine learning methods; namely, adaptive neuro-fuzzy inference system (ANFIS) combined with particle swarm optimization (PSO) and genetic algorithm (GA) for the prediction of *CO (the key intermediate) adsorption energy as the efficiency metric. The developed ANFIS-PSO and ANFIS-GA showed excellent performance with RMSE of 0.0411 and 0.0383, respectively, the minimum errors reported so far in this field. Additionally, the sensitivity analysis showed that the center and the filling of the d-band are the most determining parameters for the electrocatalyst surface reactivity. The present study conveniently indicates the potential and value of machine learning in directing the experimental efforts in alloy system electrocatalysts for CO₂ reduction.

Facet-Defined Strain-Free Spinel Oxide for Oxygen Reduction

Exposing facet and surface strain are critical factors affecting catalytic performance but unraveling the composition-dependent activity on specific facets under strain-controlled environment is still challenging due to the synthetic difficulties. Herein, we achieved a (001) facet-defined Co-Mn spinel oxide surface with different surface compositions using epitaxial growth on Co₃O₄ nanocube template. We adopted composition gradient synthesis to relieve the strain layer by layer, minimizing the surface strain effect on catalytic activity. In this system, experimental and calculational analyses of model oxygen reduction reaction (ORR) activity reveals a volcano-like trend with Mn/Co ratios because of an adequate charge transfer from octahedral-Mn to neighboring Co. Co_0.5Mn_0.5 as an optimized Mn/Co ratio exhibits both outstanding ORR activity (0.894 V vs RHE in 1 M KOH) and stability (2% activity loss against chronoamperometry). By controlling facet and strain, this study provides a well-defined platform for investigating composition-structure-activity relationships in electrocatalytic processes.

Applications of Machine Learning in Alloy Catalysts: Rational Selection and Future Development of Descriptors

At present, alloys have broad application prospects in heterogeneous catalysis, due to their various catalytic active sites produced by their vast element combinations and complex geometric structures. However, it is the diverse variables of alloys that lead to the difficulty in understanding the structure-property relationship for conventional experimental and theoretical methods. Fortunately, machine learning methods are helpful to address the issue. Machine learning can not only deal with a large number of data rapidly, but also help establish the physical picture of reactions in multidimensional heterogeneous catalysis. The key challenge in machine learning is the exploration of suitable general descriptors to accurately describe various types of alloy catalysts, which help reasonably design catalysts and efficiently screen candidates. In this review, several kinds of machine learning methods commonly used in the design of alloy catalysts is introduced, and the applications of various reactivity descriptors corresponding to different alloy systems is summarized. Importantly, this work clarifies the existing understanding of physical picture of heterogeneous catalysis, and emphasize the significance of rational selection of universal descriptors. Finally, the development of heterogeneous catalytic descriptors for machine learning are presented.

Support, composition, and ligand effects in partial oxidation of benzyl alcohol using gold–copper clusters

The effect of metallic composition, support, and ligands on catalytic performance using AuCu clusters in benzyl alcohol oxidation is investigated.

Manipulating Interfaces of Electrocatalysts Down to Atomic Scales: Fundamentals, Strategies, and Electrocatalytic Applications

Raising electrocatalysis by rationally devising catalysts plays a core role in almost all renewable energy conversion and storage systems. The principal catalytic properties can be controlled and improved well by manipulation of interfaces, ascribed to the interactions among different components/players at the interfaces. In particular, manipulating interfaces down to atomic scales is becoming increasingly attractive, not only because those atoms at around the interface are the key players during electrocatalysis, but also, understandings on the atomic level electrocatalysis allow one to gain deep insights into the reaction mechanism. With the feature down-sizing to atomic scales, there is a timely need to redefine the interfaces, as some of them have gone beyond the conventionally perceived interfacial concept. In this overview, the key active players participating in the interfacial manipulation of electrocatalysts are examined, from a new angle of "atomic interface," including those individual atoms, defects, and their interactions, together with the essential characterization techniques for them. The specific approaches and pathways to engineer better atomic interfaces are investigated, and thus to enable the unique electrocatalysis for targeted applications. Looking beyond recent progress, the challenges and prospects of the atomic level interfacial engineering are also briefly visited.

Experimental Clarification of the RWGS Reaction Effect in H2O/CO2 SOEC Co-Electrolysis Conditions

In the present investigation, modified X-Ni/GDC electrodes (where X = Au, Mo, and Fe) are studied, in the form of half-electrolyte supported cells, for their performance in the RWGS through catalytic-kinetic measurements. The samples were tested at open circuit potential conditions in order to elucidate their catalytic activity towards the production of CO (rco), which is one of the products of the H2O/CO2 co-electrolysis reaction. Physicochemical characterization is also presented, in which the samples were examined in the form of powders and as half cells with BET, H2-TPR, Air-TPO and TGA re-oxidation measurements in the presence of H2O. In brief, it was found that the rate of the produced CO (rco) increases by increasing the operating temperature and the partial pressure of H2 in the reaction mixture. In addition, the first results revealed that Fe and Mo modification enhances the catalytic production of CO, since the 2wt% Fe-Ni/GDC and 3wt% Mo-Ni/GDC electrodes were proven to perform better compared to the other samples, in the whole studied temperature range (800–900 °C), reaching thermodynamic equilibrium. Furthermore, carbon formation was not detected.

Prediction of Stable and Active (Oxy-Hydro) Oxide Nanoislands on Noble-Metal Supports for Electrochemical Oxygen Reduction Reaction

Developing cost-effective oxygen electrocatalysts with high activity and stability is key to their commercialization. However, economical earth-abundant catalysts based on first-row transition-metal oxides suffer from low electrochemical stability, which is difficult to improve without compromising their activity. Here, using density functional theory calculations, we demonstrate that noble-metal supports lead to bifunctional enhancement of both the stability and the oxygen reduction reaction (ORR) activity of metal (oxy-hydro) oxide nanoislands. We observe a significant stabilization of supported nanoislands beyond the intrinsic stability limits of bulk phases, which originates from a favorable lattice mismatch and reductive charge transfer from oxophilic supports. We discover that interfacial active sites (located between the nanoisland and the support) reinforce the binding strength of reaction intermediates, hence boosting ORR activity. Considering that both stability and activity lead to discovery of CoOOH|Pt, NiOOH|Ag, and FeO₂|Ag as viable systems for alkaline ORR, we then use a multivariant linear regression method to identify elementary descriptors for efficient screening of promising cost-effective nanoisland|support catalysts.

Noble metal supported hexagonal boron nitride for the oxygen reduction reaction: a DFT study

Discovering active, stable and cost-effective catalysts for the oxygen reduction reaction (ORR) is of utmost interest for commercialization of fuel cells. Scarce and expensive noble metals such as Pt and Pd are the state-of-the-art active ORR catalysts but suffer from low stability against CO poisoning. Hexagonal boron nitride (h-BN) is a particularly attractive material due to its low cost and stability; however, it suffers from intrinsic low activity toward the ORR in the pristine form as a result of its inherently low conductivity with a large band gap of ∼5.5 electron volts. During the past few years, several strategies such as using metal supports, metal doping and atomic vacancies have been reported to significantly increase the conductivity, thereby promoting the ORR activity. Herein we use density functional theory calculations to systematically study these strategies for activating inert h-BN and further examine the stability against CO poisoning. We show that noble metals, such as Ag, Pd, and Pt, require boron (B) or nitrogen (N) vacancies to reasonably activate h-BN toward the ORR. For example, Pd supported h-BN with B-vacancies exhibits significantly high ORR activity. All three examined metal supported h-BNs are predicted to be stable against CO poisoning. These results demonstrate that supporting h-BN on noble metals is a promising strategy to increase the stability against CO poisoning while maintaining high ORR activity.

Recent advancements in Pt-nanostructure-based electrocatalysts for the oxygen reduction reaction

A comprehensive evaluation of Pt-nanostructure-based electrocatalysts for the oxygen reduction reaction.

Computational exploration of borophane-supported single transition metal atoms as potential oxygen reduction and evolution electrocatalysts

Novel monolayer-boron (borophene) is a recent addition to the family of 2D materials. In particular, full surface hydrogenation of triangular borophene (borophane (BH)) to passivate empty p orbitals in boron is identified as producing a new stable 2D material that possesses direction-dependent Dirac cones similar to graphene. By a series of density functional theory (DFT) computations, we investigated the potential of single transition metal atoms supported on borophane with vacancies (the TM-BH system) as an efficient ORR/OER electrocatalyst for applications in renewable energy technologies. In TM-BH systems, the coupling of d-orbitals of the TM dopant with the p-orbitals of surrounding boron atoms results in an increase in the density of states near the Fermi-level generating active sites to facilitate the ORR/OER via an efficient four-electron transfer mechanism. Among the considered TM-BH systems, Fe-BH and Rh-BH were found to be promising ORR electrocatalysts with overpotentials (ηORR) of 0.43 V and 0.47 V, respectively, whereas, for the OER, Rh-BH with 0.24 V has the smallest ηOER value followed by Co-BH (0.37 V), under the equilibrium electrode potential. These ηORR and ηOER values indicate higher activities than the current most active ORR (Pt(111) (0.63 V)) and OER (rutile-type RuO2 (0.37 V)) electrocatalysts.

Active learning with non-ab initio input features toward efficient CO2 reduction catalysts

In this work, we propose the use of the d-band width of the muffin-tin orbital theory (to account for the local coordination environment) plus electronegativity (to account for adsorbate renormalization) as a simple set of alternative descriptors for chemisorption which do not require ab initio calculations for large-scale first-hand screening.

In a conventional chemisorption model, the d-band center theory (augmented sometimes with the upper edge of the d-band for improved accuracy) plays a central role in predicting adsorption energies and catalytic activity as a function of the d-band center of solid surfaces, but it requires density functional calculations that can be quite costly for the purposes of large scale screening of materials. In this work, we propose to use the d-band width of the muffin-tin orbital theory (to account for the local coordination environment) plus electronegativity (to account for adsorbate renormalization) as a simple set of alternative descriptors for chemisorption which do not require ab initio calculations for large-scale first-hand screening. This pair of descriptors is then combined with machine learning methods, namely, neural network (NN) and kernel ridge regression (KRR). We show, for a toy set of 263 alloy systems, that the CO adsorption energy on the (100) facet can be predicted with a mean absolute deviation error of 0.05 eV. We achieved this high accuracy by utilizing an active learning algorithm, without which the accuracy was 0.18 eV. In addition, the results of testing the method with other facets such as (111) terrace and (211) step sites suggest that the present model is also capable of handling different coordination environments effectively. As an example of the practical application of this machine, we identified Cu₃Y@Cu* as an active and cost-effective electrochemical CO₂ reduction catalyst to produce CO with an overpotential ∼1 V lower than a Au catalyst.